{"title":"Predicting Distant Metastasis in Papillary Thyroid Carcinoma: A Postoperative Nomogram Integrating Sex, Histology, Bilaterality, and Extrathyroidal Extension.","authors":"Jiaxi Wang, Zhiyu Li, Chuang Chen","doi":"10.2147/IJGM.S547317","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Papillary thyroid carcinoma (PTC) generally has a good prognosis, but distant metastasis (DM) significantly reduces survival. Existing predictive models for DM have limited accuracy. This study aimed to identify independent risk factors for DM in PTC and develop a clinical prediction model using routine pathological parameters.</p><p><strong>Methods: </strong>We retrospectively analyzed a cohort of 4127 PTC patients who underwent surgery between 2017 and 2022. Patients were divided into DM (n = 30) and non-DM (n = 4097) groups. Key variables, including sex, age, pathological subtype, tumor size, bilaterality, multifocality, extrathyroidal extension (ETE), and lymph node metastasis (LNM), were collected. We used univariate and multivariate logistic regression to identify independent predictors. A nomogram model was built and its performance was evaluated using ROC curves and other metrics.</p><p><strong>Results: </strong>Univariate analysis identified male sex (OR = 0.362, <i>p</i> = 0.006), solid variant (OR = 36.509, <i>p</i> < 0.001), Multifocal (OR = 0.247, <i>p</i> < 0.001), bilaterality (OR = 2.847, <i>p</i> = 0.004), and ETE (OR = 4.360, <i>p</i> = 0.016) as significant risk factors. Multivariate analysis confirmed male sex (OR = 0.434, <i>p</i> = 0.029), solid variant (OR = 23.483, <i>p</i> < 0.001), bilaterality (OR = 1.309, <i>p</i> = 0.047), and ETE (OR = 3.094, <i>p</i> = 0.012) as independent predictors. The nomogram model showed a moderate discriminative ability with an AUC of 0.737, a sensitivity of 66.7%, and a specificity of 68.7%.</p><p><strong>Conclusion: </strong>In this large-scale Chinese cohort study, we identified male sex, solid variant, bilaterality, and ETE as independent risk factors for PTCDM. The resulting model offers a practical tool for postoperative risk assessment, which can help guide customized surveillance and treatment for high-risk patients. Future research should focus on validating this model with external and multicenter cohorts and incorporating molecular biomarkers to further improve its predictive accuracy.</p>","PeriodicalId":14131,"journal":{"name":"International Journal of General Medicine","volume":"18 ","pages":"5409-5419"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439841/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of General Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/IJGM.S547317","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Papillary thyroid carcinoma (PTC) generally has a good prognosis, but distant metastasis (DM) significantly reduces survival. Existing predictive models for DM have limited accuracy. This study aimed to identify independent risk factors for DM in PTC and develop a clinical prediction model using routine pathological parameters.
Methods: We retrospectively analyzed a cohort of 4127 PTC patients who underwent surgery between 2017 and 2022. Patients were divided into DM (n = 30) and non-DM (n = 4097) groups. Key variables, including sex, age, pathological subtype, tumor size, bilaterality, multifocality, extrathyroidal extension (ETE), and lymph node metastasis (LNM), were collected. We used univariate and multivariate logistic regression to identify independent predictors. A nomogram model was built and its performance was evaluated using ROC curves and other metrics.
Results: Univariate analysis identified male sex (OR = 0.362, p = 0.006), solid variant (OR = 36.509, p < 0.001), Multifocal (OR = 0.247, p < 0.001), bilaterality (OR = 2.847, p = 0.004), and ETE (OR = 4.360, p = 0.016) as significant risk factors. Multivariate analysis confirmed male sex (OR = 0.434, p = 0.029), solid variant (OR = 23.483, p < 0.001), bilaterality (OR = 1.309, p = 0.047), and ETE (OR = 3.094, p = 0.012) as independent predictors. The nomogram model showed a moderate discriminative ability with an AUC of 0.737, a sensitivity of 66.7%, and a specificity of 68.7%.
Conclusion: In this large-scale Chinese cohort study, we identified male sex, solid variant, bilaterality, and ETE as independent risk factors for PTCDM. The resulting model offers a practical tool for postoperative risk assessment, which can help guide customized surveillance and treatment for high-risk patients. Future research should focus on validating this model with external and multicenter cohorts and incorporating molecular biomarkers to further improve its predictive accuracy.
期刊介绍:
The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas.
A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal.
As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.